code: def test_compute_fid_sqrtm():
      mu1 = torch.tensor([0, 0])
      mu2 = torch.tensor([0, 0])
  
      sigma1 = torch.tensor([[-1, 1], [1, 1]], dtype=torch.float64)
      sigma2 = torch.tensor([[1, 0], [0, 1]], dtype=torch.float64)
  
      with pytest.raises(ValueError, match=r"Imaginary component "):
          fid_score(mu1, mu2, sigma1, sigma2)
  
      sigma1 = (
          torch.ones((2, 2), dtype=torch.float64)
          * torch.finfo(torch.float64).max
      )
      sigma2 = torch.tensor([[1, 0.5], [0, 0.5]], dtype=torch.float64)
  
      assert torch.isinf(
          torch.tensor(fid_score(mu1, mu2, sigma1, sigma2))
      )
  
imports: import os
  import re
  from unittest.mock import patch
  import pytest
  import pytorch_fid.fid_score
  import scipy
  import torch
  from numpy import cov
  import ignite.distributed
  from ignite.metrics.gan.fid import FID, fid_score
label_desc: Use image node_id 8 for calling a global function with example usage: test_compute_fid_sqrtm() without return types